(Hyper)-Graphical Models in Biomedical Image Analysis

Nikos Paragios 1, 2 Enzo Ferrante 3 Ben Glocker 3 Nikos Komodakis 4 Sarah Parisot 3 Evangelia I. Zacharaki 1, 2
4 IMAGINE [Marne-la-Vallée]
LIGM - Laboratoire d'Informatique Gaspard-Monge, CSTB - Centre Scientifique et Technique du Bâtiment, ENPC - École des Ponts ParisTech
Abstract : Computational vision, visual computing and biomedical image analysis have made tremendous progress over the past two decades. This is mostly due the development of efficient learning and inference algorithms which allow better and richer modeling of image and visual understanding tasks. Hyper-Graph representations are among the most prominent tools to address such perception through the casting of perception as a graph optimization problem. In this paper, we briefly introduce the importance of such representations, discuss their strength and limitations, provide appropriate strategies for their inference and present their application to address a variety of problems in biomedical image analysis.
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Nikos Paragios, Enzo Ferrante, Ben Glocker, Nikos Komodakis, Sarah Parisot, et al.. (Hyper)-Graphical Models in Biomedical Image Analysis. Medical Image Analysis, Elsevier, 2016, ⟨10.1016/j.media.2016.06.028⟩. ⟨hal-01359107⟩

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